Machine Learning (ML) has transformed traditional computing by enabling machines to learn from data. Machine Learning algorithms have built-in smarts to use available data to answer questions. Apart from using data to learn, ML algorithms can also detect patterns to uncover anomalies and provide solutions. Insecurity environments, Machine Learning can move one step ahead of humans and trap missed security breaches in public systems. Machine Learning use cases are being refined every day, with the potential for predicting unforeseen events much before they happen and even suggesting probable remedial actions.
Adoption of Machine Learning
In recent years, with the advancement of Artificial Intelligence (AI) science and the application development with Machine Learning algorithms has reached new heights. Today, we are looking forward to a robust algorithm economy, where even a small, ordinary business person can buy packaged algorithms designed as business solutions. The sudden commercialization of ML has been possible largely due to the availability of superior and cheaper hardware, processing architectures, and rise of supporting technologies like Big Data and Hadoop.
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According to the DATAVERSITY® Webinar Machine Learning (ML) Adoption Strategies, the ML applications market is steadily maturing and users have to select the right approach and solutions from the available pool of applications to make a particular ML-powered, business solution work within their own environments.
Improved Machine Learning Use Cases
With the rising popularity of “smart” applications or systems that take the labor out of routine BI, more and more businesses are willing to partner with ML application vendors to partially or wholly automate their advanced BI systems. DATAVERSITY’s Machine Learning, Data Modeling, and Testing indicates that rapid automation of such tasks like Data Modeling has vastly reduced the complexity of using these ready-made ML solutions.
While in the traditional enterprise BI scenario, experienced Data Scientists spent hours of labor detecting patterns from existing data to predict future outcomes, the smart ML-powered BI applications today can deliver instant answers to complex business queries. This article cites the example of BeyondCore, which has the capability for creating Data Models for various types of analysis. Specifically, in data discovery solutions, application vendors are providing automated Data Modeling functions to assist advanced Business Intelligence functions.
Machine Learning algorithms have been around for quite some time, but the capability for “Unsupervised Learning” coupled with Big Data has catapulted ML powered, BI systems to a new era of Data Analytics. In this SAS article, the author establishes that today’s Machine Learning science has gone as far as to support “iterative learning” from new data.
Machine Learning Use Cases to Boost Business
As AI, ML, and Deep Learning technologies continue to evolve, business adoption of data technologies will happen faster and across the global business landscape, not just in large enterprises. The ultimate goal of data solution providers preaching AI use cases is to bring partially ready-made solutions at an affordable cost to the hands of medium and small business owners, so that these technologies have the widest reach.
The other noticeable trend is that Machine Learning use cases are rapidly growing across verticals and the mainstreaming of Big Data, Cloud, IoT, and Hadoop have expedited the growth and implementation of such use cases. Today, large, medium, and small businesses have the capability to access and implement “smart” tools for personalized marketing, risk and fraud analysis, predictive equipment maintenance, to name a few.
AI systems are certainly not full proof, but eventually these new data technologies will collectively transform the business BI landscape. Also read Analytics Teams Eye Machine Learning Use Cases to Boost Business to find out about other recent developments in AI and ML technologies.
Machine Learning Use Cases for Predictive Analytics
With Machine Learning, traditional Predictive Analytics have been replaced by multi-tier probabilistic forecasting, where each predicted outcome has an associated probability measure resulting in a series of probabilities rather than a single probability. This kind of forecasts can be very useful in the energy industry.
The DZone article titled Top 4 Machine Learning Use identifies four key areas for the energy industry where ML algorithms can be used for enhanced energy management. These four areas of Predictive Analytics include estimating power loads, forecasting prices, predicting wind power generation, and predicting solar power generation.
Machine Learning in AI Applications
The “adaptive” nature of AI technologies has made the widespread adoption of smart BI solutions across verticals possible. DATAVERSITY’s Artificial Intelligence Use Cases Overview suggests that Machine Learning use cases are rapidly growing in the Data Management industry with robots, and sensor-driven machines taking over human functions in manufacturing, finance, legal, energy, healthcare, and shipping industries among others.
The use of very high volumes of data in these industry sectors has led Intel to claim that by 2020, their servers “will process more data analytics than other types of data jobs.” Intel’s Develop Education Program further promotes that advanced ML or DL algorithms can assist AI applications to deliver completely unbiased, data-driven decisions.
The need of the hour is for the industry leadership to leverage AI use cases as the game changer for enhanced business efficiency leading to increased top-line growth.
Techemergence’s AI Industry Overview, marketing, finance, and healthcare are the top three industry sectors dealing with “multi-structured data.” According to this overview report, five industry sectors – financial services, legal services, marketing, retail, and advertising, have achieved significant cost reductions and increased efficiency with AI technologies, systems, and power tools. The biggest beneficiary of this practice is the consumer himself because now his decision-making process is assisted by these powerful and insightful technologies.
A few instances of successful implementation of AI use cases:
- The financial services sector is routinely using NLP, data mining, and ML algorithms.
- GE is using a sensor-driven, networked data acquisition and analytics system that captures data from many “operational touch-points” for advanced intelligence.
- The learning industry is utilizing AI technologies in its online classrooms and in digital course
The Artificial Intelligence Market Forecasts 2016 -2025 across 27 Industry Sectors provides a nutshell view of AI use cases including the implementation of Machine Learning, Deep Learning, NLP, computer vision, and associated technologies.
The DATAVERSITY Webinar Machine Learning – From Discovery to Understanding explored how Machine Learning has become the AI industry standard for pattern recognition. In the next lap, technology companies will concentrate on applications that use ML algorithms to decipher meaning out of their discoveries.
Machine Learning Use Cases in Data Management
Machine Learning is now widely used to manage data across all business verticals. Data management cannot be regarded as a separate industry sector as it pervades each and every industry. With the phenomenal growth and popularity of data technologies in the recent years, the rising trends of “smart Data Management solutions” are here to stay and prosper. You can picture the widespread utilization of ML in Data Management, a resource that how modern technologies and tools have enhanced the business benefits across the data value chain.
Industry-Wide Machine Learning Use Cases
In this section, some industry-specific ML use cases are explored:
- Machine Learning for Managing Healthcare Data
With healthcare providers steadily investing in Big Data technologies, AI and ML systems will now have a field day in the global healthcare industry. Natural language processing (NLP) and Deep Learning (DL) are just beginning to invade the highly-advanced world of medical diagnostics, where clinical data analysis and imaging technologies have been newly strengthened by the power of Machine Learning. Where archaic analytics tolls failed to extract insights from images, voice recordings, or EHR system reports, ML has eased in with powerful algorithms to extract meaning from all these diverse data sources.
In Top 4 Machine Learning Use Cases for Healthcare Providers, you will discover that Weill Cornell Medical School and Carnegie Mellon University are jointly developing ML solutions to deliver enhanced healthcare outcomes. With the future growth of Big Data technologies, the possibilities are endless.
- Machine Learning for Managing Finance Data
Banks and allied financial services businesses use ML solutions primarily for two purposes—to extract intelligence from data, and to detect fraud. More and more global fintech companies are saying goodbye to legacy systems. Now that AI and Machine Learning are in, financial businesses are looking to build custom solutions.
As the article titled Machine Learning in Finance reveals, traditional fintech BI systems depended on “static data” like loan applications and financial reports to determine loan eligibility for customers. In sharp contrast to such practices, Machine Learning algorithms can learn from the customer’s financial history and analyze the impact of certain market trends or sudden developments on the customer’s financial status.
This type of analysis helps uncover bad investors very quickly. Some well known names in the financial world such as JPMorgan and Morgan Stanley have already gone a step further by developing digital, ML-powered investment advisors, who provide assisted financial advisory services. On the flip side, as ML systems become more proficient in monitoring security issues of consumer accounts and delivering better risk management, the systems will work more in favor of consumers than in favor of financial companies.
- Machine Learning for Managing Marketing and Customer Data
The Growing Role of AI and Machine Learning in Marketing and Customer Engagement suggests that with the ever-growing volume of unstructured data on social media, prospective companies can mix “social listening technologies” to filter mentions and AI tools to conduct sentiment analysis.
In What Are the Top 10 Use Cases for Machine Learning?, you will find that ML algorithms with natural language characteristics may soon replace the human customer service representatives and bring in a new era of automated customer service in near future.
AI and ML together have a bright future in taking the predictive technologies to the next era of event-based warnings and alerts. The unique capability of ML technology to process data, detect patterns, and co-relate human behavior makes it the single answer to developing smart digital assistants across industries from banking and finance to healthcare.
Conclusion: The Future of Data Management
This article establishes that Machine Learning use cases will continue to play a crucial role in the future of enterprise Data Management. The article The Immediate Future of Data Management discusses how since 2014, Machine Learning has continuously improved its predictive capabilities, which can be effectively used across verticals to enhance eCommerce.
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